1. Field of the Invention
The present invention relates to an image dependent gamut mapping method for mapping source colors of an image to destination colors reproducible by a printing apparatus, said source colors and destination colors being part of a color space comprising a lightness axis, each source color having a lightness component within a range between a source white point and a source black point on the lightness axis, and each destination color having a lightness component within a range between a destination white point and a destination black point on the lightness axis, the method comprising the steps of determining a composed transfer function which maps the source white point onto the destination white point and the source black point onto the destination black point and applying the composed transfer function on each lightness component of source colors of at least a part of the image.
2. Background of the Invention
A digital image consists of pixels that are defined as points in a color space, e.g. an RGB color space, an XYZ color space or an L*a*b* color space. In an RGB color space, each pixel is defined by three coordinates that represent the red, green and blue color component of the pixel respectively. In a L*a*b* color space each pixel is defined by three coordinates which represent the lightness of the pixel, its position between red/magenta and green and its position between yellow and blue, respectively. Each coordinate represents a property of each point in the color space.
A gamut may be defined as a specific part of the color space. For example, an image gamut of a digital image may be defined as the part of the color space that contains only those points of the color space, which are also part of the representation of the colors of the digital image in the color space. A printer gamut of a printing apparatus may be defined as the part of the color space that contains all of the colors that can be printed by the printing apparatus. The printer gamut is often smaller in size that the image gamut. This means that a point of the image gamut may be outside the printer gamut and cannot be printed according to the coordinates of that point. Normally, points of the image gamut of a first digital image are mapped on the points of the printer gamut in a particular way. This mapping results in a second digital image and the printing apparatus is able to print this second digital image.
It is recommended to implement a mapping from a first digital image to a second digital image, as described above, in such a way that at least one property of the pixels in the digital image is maintained; for example, if one wants to maintain a perceived lightness of the first digital image or local details of the first digital image. An algorithm for mapping digital images may be used in many of the currently available color management software, which may be implemented in a controller of a printing apparatus. Often, such an algorithm contains a mapping of a color space towards the same color space, which is decomposed into a scaling step in the directions of the three coordinate axes of the color space and an offset step. Such an algorithm scales the complete digital image gamut in three directions and normally takes into account the possible ranges of the coordinates in the printer gamut. Such an algorithm may take into account a deviating axis of a property of the printer gamut, e.g. a lightness axis. This results in a so-called black and white point correction algorithm. A printer displays color images in its unique way. A printer may be calibrated using look-up tables (LUTs) or with an ICC style color management, which relies on color profiles to ensure that images are reproduced and displayed accurately. Part of creating an LUT may be a black and white point correction.
A black and white point correction is an operation that matches perceived black (black point of the first image) to the darkest printer lightness and perceived white (white point of the first image) to the brightest printer lightness. It is critical for a pleasing reproduction of images. Black and white point correction may be automatically included in a gamut mapping algorithm or it may be performed as a pre- or post-processing step in addition to a gamut mapping.
Such a gamut mapping may be defined by using a solution of a optimization problem as, for example, disclosed in publication “Space sensitive color gamut mapping: A variational approach”/R. Kimmel, D, Shaked, M. Elad and I. Sobel (HP) or in publication “A framework for image-dependant gamut mapping”, in Proc. SPIE, Color Imaging/J. Giesena, E. Schuberth, K. Simon, D. Zeiter and P. Zolliker (Gretag).
These publications define a gamut mapping as an outcome of a parameterized mathematical optimization problem that allows constraint of the degree to which objectives like contrast preservation, hue preservation, saturation preservation and the continuity of the mapping can be violated while maximizing the printer gamut exploitation. A disadvantage of the approach is that the optimization problem is defined over millions of pixels and therefore computationally rather expensive.
In a publication of N. Moroney (HP), “Local color correction using non-linear masking” in Proc. IS&T/SID Eighth Color Imaging Conference, a local contrast enhancement algorithm is described that is based on non-linear masking. The algorithm is equivalent to deriving a specific tone reproduction curve for each pixel in the image. The work focuses on image enhancement in total and comprises an estimation of a compression function per processed pixel, which is rather cumbersome.
A starting point for the method according to the present invention is that the lightness axis of the color space comprises the source white point, the source black point, the destination white point and the destination black point. The method comprises the steps of defining a composed transfer function which maps the source white point onto the destination white point and the source black point onto the destination black point, and applying the function on the lightness components of each source color. Such functions are known from the prior art, and may be visualized by a sigmoidal function or another continuous function.